3 papers
High-Fidelity Modeling of Stochastic Chemical Dynamics on Complex Manifolds: A Multi-Scale SIREN-PINN Framework for the Curvature-Perturbed Ginzburg-Landau Equation
Julian Evan Chrisnanto, Salsabila Rahma Alia, Nurfauzi Fadillah +1
The accurate identification and control of spatiotemporal chaos in reaction-diffusion systems remains a grand challenge in chemical engineering, particularly when the underlying ca…
Intrinsic-Metric Physics-Informed Neural Networks (IM-PINN) for Reaction-Diffusion Dynamics on Complex Riemannian Manifolds
Julian Evan Chrisnanto, Salsabila Rahma Alia, Nurfauzi Fadillah +1
Simulating nonlinear reaction-diffusion dynamics on complex, non-Euclidean manifolds remains a fundamental challenge in computational morphogenesis, constrained by high-fidelity me…
ASPEN: An Adaptive Spectral Physics-Enabled Network for Ginzburg-Landau Dynamics
Julian Evan Chrisnanto, Nurfauzi Fadillah, Yulison Herry Chrisnanto
Physics-Informed Neural Networks (PINNs) have emerged as a powerful, mesh-free paradigm for solving partial differential equations (PDEs). However, they notoriously struggle with s…